Graphical language for optimization and use

ABSTRACT

The present invention provides novel techniques for graphically modeling, displaying, and interacting with parametric hybrid models used to optimize and control components of industrial plants and enterprises. In particular, a graphical modeling tool of a control/optimization system for controlling a plant or enterprise is configured to transmit a graphical user interface to a user, wherein the graphical user interface enables a plurality of command inputs relating to a plurality of parametric hybrid models based on a security access level of the user. The parametric hybrid models may be displayed by the graphical user interface as nodes of a network with connections connecting the nodes. The user may graphically manipulate the nodes and connections associated with the parametric hybrids models to either modify optimization constraints of the model network, or actually modify the manner in which the parametric hybrid models function (e.g., inputs, outputs, parameters, and so forth, of the parametric hybrid models), depending on the access level of the user.

BACKGROUND

The present invention generally relates to modeling for optimization andcontrol of industrial plants and enterprises. More particularly, thepresent invention relates to systems and methods for graphicallymodeling, displaying, and interacting with parametric hybrid models usedto optimize and control the operation of industrial plants andenterprises and/or the operation of some of their components.

BRIEF DESCRIPTION

The current state of operation in an industrial facility, such as amanufacturing plant, an oil refinery, a power plant, or even a utilityplant on a college campus, treats planning, scheduling, and control(manual or automatic) as separate disciplines. In particular, theprevailing practice views planning and scheduling as offline (i.e., notduring operation of the plant) activities that serve as inputs to anonline (i.e., during operation) component of the plant (i.e., operatoror control system actions).

This separation introduces significant challenges to the robustness,cost-effectiveness, and environmental footprint of the operation, andhas long been recognized by plant operation personnel and managers, aswell as business management personnel. A solution to this challenge,however, has proven elusive. Despite significant investment in sensingand control infrastructures, database systems, and business managementsoftware, creating a production schedule that meets “current” financialgoals of the enterprise and respects the operational constraints of theplant has remained a formidable challenge.

The plant operation is often given a schedule that is either notfeasible or does not optimally account for the current operatingconditions of the plant. When faced with this challenge, plant operationis unable to contribute to the modification of the outdated schedule, asthe model used for scheduling is viewed as a “black box” to the plantoperation. To add to the complexity, the model typically relates to theentire plant, and the knowledge base for the model is thus distributedthroughout the plant. Consistent modification of the relevant model froma distributed set of expertise throughout the plant has proven asignificant challenge. Furthermore, the scheduling problem setup andexecution is also a cumbersome process for which the required expertiseis not widely available.

DRAWINGS

These and other features, aspects, and advantages of the presentinvention will become better understood when the following detaileddescription is read with reference to the accompanying drawings in whichlike characters represent like parts throughout the drawings, wherein:

FIG. 1 is a schematic diagram of an exemplary commercial or industrialenergy system;

FIG. 2 is a block diagram of exemplary components of the energy systemof FIG. 1, illustrating various interconnections;

FIG. 3 is a block diagram of an exemplary parametric hybrid model formodeling the energy system of FIG. 1;

FIG. 4 is a block diagram of an exemplary evaporation chiller block ofFIG. 2;

FIG. 5 is a block diagram of an exemplary boiler block of FIG. 2;

FIG. 6 is an example of a graphical user interface (i.e., a graphicalrepresentation) of a graphical modeling tool representing a plurality ofparametric hybrid models relating to components of the system of FIG. 1arranged as a network;

FIG. 7 is a block diagram of an enterprise-integrated parametric hybridmodel enabled control system for controlling the system of FIG. 1;

FIG. 8 is an example of the graphical user interface (i.e., a graphicalrepresentation) of the graphical modeling tool illustrating a library ofcomponent blocks available to a user;

FIG. 9 is an example of the graphical user interface (i.e., a graphicalrepresentation) of the graphical modeling tool illustrating anoptimization view when an Optimization tab is selected by the user;

FIG. 10 is an example of the graphical user interface (i.e., a graphicalrepresentation) of the graphical modeling tool illustrating theoptimization view when the user has submitted a command input and theoptimization solution of the system of FIG. 1 has been updated;

FIG. 11 is an example of a non-linear and non-convex optimizationproblem and two convex approximations for the problem;

FIG. 12 is an example of a solution graph for optimization solutionequations using the parametric hybrid models; and

FIG. 13 is an example of a method for utilizing the graphical userinterface to interact with the parametric hybrid models.

DETAILED DESCRIPTION

As described above, there is often a discontinuity between the generallyoffline (i.e., not during operation) planning and scheduling activitiesof a plant and the generally online (i.e., during operation of theplant) control and operation activities of the plant. The embodimentsdescribed herein address the three main challenges that have contributedto the persistence of this deficiency. First, the embodiments describedherein provide a versatile modeling framework for representing an entireplant and, indeed, an entire enterprise including one or more plants.Existing modeling frameworks are generally unable to: (a) capturerelevant details of plant operation as it pertains to economicobjectives of the enterprise, (b) avoid prohibitive complexity given thenumber of components to be included in the models that represent theplants, and (c) maintain modularity such that there is an intuitivecorrespondence between the components of the physical plants/processesand the model components. The embodiments described herein address thesechallenges by employing a parametric hybrid modeling framework, such asthat disclosed in U.S. patent application publication number2005/0187643, which is hereby incorporated by reference in its entirety.

Second, the embodiments described herein address the conventionalseparation of the offline interactions with the models (e.g., modelbuilding, planning, scheduling interactions) that represent the plants,and the online interactions (e.g., the control and operationinteractions) with the models. In particular, in conventional systems,the deployed models are not transparent to all users. In other words,the quality of the models and their components are not easily measurableor accessible as the models are deployed to an online environment. Inthese conventional systems, modification of the models is generally anoffline exercise, and the expertise for modifying the models isgenerally highly centralized. However, in reality, the people who arequalified to modify one component of a model may have no qualificationto modify another component of the model, and these different peopleoften physically reside in different locations. Generally speaking,asynchronous modification of the model components is not possible, andmodification frequency widely varies depending on the model type,operation scenario, and so forth. The embodiments described hereinaddress these challenges by employing a transparent model deploymentstrategy.

Third, the embodiments described herein provide a graphical optimizationlanguage that eliminates the communication barrier between optimizationsoftware and end users (plant operators, accounting department,financial department, and so forth). In particular, the graphicallanguage for optimization enables a lower level of competency toimplement and/or deploy optimization solutions. In other words, ratherthan requiring a Ph.D. with an optimization background, a plant managerwith process knowledge will be able to own the optimization solution. Inaddition, the graphical language provides distributed development,deployment, and maintenance capabilities such that the composition ofthe optimization problem and subsequent modifications to theoptimization problem may be carried out with input from relevantstakeholders in their normal operation settings.

The embodiments described herein enable the handling of various aspectsof operation (e.g. accommodation of scheduled maintenance for keycomponents, robustness with respect to disruptions in the supply chainor available capacity, energy efficiency and low environmental footprintof the operation, responsiveness to market pricing pressures, and soforth) in a systematic manner with full transparency of the objectives,priorities, and constraints of the underlying models. In particular, theembodiments described herein enable graphical setup, execution, andreporting of large-scale (potentially non-linear) optimization problemsin a manner that the plant-wide and/or enterprise-wide optimizationsolutions may be simultaneously managed by a distributed set ofstakeholders without the need for a centralized authority to act as agatekeeper of the information and transactions. To achieve thisobjective, the embodiments described herein include core enablingalgorithmic concepts, as well as a software implementation methodology.

As described above, the embodiments described herein have many potentialapplication scenarios. For example, the embodiments described hereinfacilitate improved planning and scheduling of operations in anindustrial plant. Complex applications such as providing steam, chilledwater, and electricity to complex energy users (e.g., petrochemicalcomplexes, university campuses, large residential complexes, and soforth) involve constant decisions by plant operation personnel, such aswhich resources should be utilized, what set points for the resources(e.g., capacity) should be set, for how long the resources shouldoperate, what existing or impending constraints should be avoided, andso forth. The complexity of the decision making in such applicationsjustifies the need for a systematic optimization solution, but thechallenges described above have heretofore impeded the development of afully functional solution.

In addition, the embodiments described herein also facilitate multi-unitoptimization in an industrial plant. Complex processes ranging frompowder milk drying in a dairy plant to boiler operation in a power plantare inherently multi-unit operations that may benefit from a principledoptimization strategy to improve, for example, the energy efficiency ofoperation, reduce the cost of response to process disturbances, improvethe ability to respond profitably to changes in market conditions, andso forth.

Furthermore, the embodiments described herein facilitate theoptimization of product compositions given acceptable recipealternatives. Many manufacturing operations involve producing endproducts that may be reached via alternative recipes (e.g., cheesemanufacturing in a dairy plant). The embodiments described hereininclude a principled approach to optimal scheduling of the manufacturingprocess such that, at any given time, the end product having apredetermined quality specification is made with the optimal set ofingredients.

The embodiments described herein also facilitate optimizing buy and/orsell decisions for an industrial plant on an electric grid. Many largeconsumers of electricity, such as industrial plants or universitycampuses, have in-house generation capacity. The economics of thein-house generation versus purchase from an electric grid is growingincreasingly more complex as utility companies move away from fixedpricing in order to maximize their profitability. The current trend insmart grids, where each node on the electric grid may perform as bothsource (i.e., provider of power) and sink (i.e., consumer of power),further complicates the decision making process. A principledoptimization solution may assist such customers to make the mostfavorable decisions at any given time given their priorities andobjectives.

The embodiments described herein include several aspects that enable theapplications described above. For example, the embodiments describedherein provide online transparency to model quality and performance.Without the ability to investigate model quality (both for individualunits, and for a network built using these units), model fidelity maynot be sustained. For example, with a purely empirical modelingparadigm, it may not be possible to pinpoint a source of qualitydeterioration and, hence, online visibility of the models may not befully achieved. A detailed first-principles based model may suffer fromthis lack of transparency. In addition, the ability to modify a targetedcomponent of a deployed model without forcing deactivation of the modelis highly desirable. The online modification of the transparent modelsin this embodiment includes and surpasses that of parameter adaptation,and encompasses the inclusion of a new parameterized model to replace anearlier underperforming parameterized model. Therefore, the onlinetransparency described herein generally improves model quality andperformance.

In addition, the embodiments described herein provide for asynchronousauthoring capability for the problem formulation by a distributed set ofusers. The large scale of the optimization problem, and the limitedscope of responsibility and competency for plant operators andengineers, makes distributed asynchronous authoring of the problemstatement desirable (and often necessary). For example, in a utilityplant, a chilled water loop and a steam loop are operationally coupled.The experts that understand the chilled water loop generally know verylittle about the steam loop operation, and most likely are not allowedand/or do not want to assume responsibility for the operation of thesteam loop, and vice versa. The distributed authoring capability shouldalso apply to the outcome of the optimization solution. The outcome ofthe plant-wide and/or enterprise-wide optimization solution (e.g., aGantt chart of operation schedules for chillers of a utility plant) ispresentable to a distributed set of users (e.g., operators, plantmanagers, and so forth). In addition, authorized stakeholders areenabled to edit proposed schedules without creating inconsistencies.Furthermore, the distributed users are enabled to update operationalconstraints and request rescheduling in a consistent manner.

The embodiments described herein also provide graphical authoringcapabilities for the problem formulation by the distributed set ofusers. Without graphical editing capability, a typical plant operatorwould not be able to directly contribute to model maintenance. Inaddition, without a graphical language for defining the optimizationproblem or interpreting the solver decisions, a typical plant operatoror engineer would not be able to contribute to a meaningful definitionof the optimization problem. The graphical authoring capabilitydescribed herein also applies to the outcome of the overall optimizationproblem. The outcome of the plant-wide and/or the enterprise-wideoptimization solution (e.g., a Gantt chart of operation schedules forchillers) is presentable to the distributed set of users (e.g.,operators, plant managers, and so forth). The authorized stakeholdersmay graphically edit the proposed schedules without creatinginconsistencies. In addition, the distributed set of users maygraphically update operational constraints and request rescheduling in aconsistent manner. The intuitiveness of the graphical authoringcapability enhances usability and uptime of the optimization solution.

In addition, the embodiments described herein incorporate real-timemeasurements and information from the plant floor and/or businesssystems. In a plant-wide and/or enterprise-wide optimization, thenetwork is often composed of a large number of component models, complexnetwork connectivity, and a dynamic set of operational conditions,constraints, and objectives. The information needed to keep this“problem formulation” up-to-date is obtained from sources that aredistributed throughout the enterprise, and often function with localautonomy. A solution that requires centralized information handling maybecome untenable. In particular, real-time measurements influence themodels in the problem formulation (e.g., efficiency curves often changebased on the current operating condition of the equipment). The abilityto achieve integration with real-time measurements can be an obstacle tothe successful adoption of plant-wide optimization solutions. Modeltransparency facilitates successful incorporation of real-timeinformation as the changes may be viewed by all relevant stakeholders.

Turning now to the drawings, FIG. 1 is a schematic diagram of anexemplary commercial or industrial energy system 10. As described ingreater detail below, the energy system 10 of FIG. 1 is an example ofthe types of plants that may benefit from the graphical modelingframework described herein. FIG. 1 illustrates the various energygeneration and consumption components that are typical in commercial andindustrial energy systems. For example, FIG. 1 includes boilers 12 thatare configured to receive fuel and generate steam for use as a source ofpower in other components of the energy system 10. For example, incertain embodiments, the steam produced by the boilers 12 may be used bycogeneration units 14 to drive generators 16, which generate electricalpower that may be consumed by components of the energy system 10 and/orsold to an electrical grid 18. In addition, in certain embodiments, aheat recovery steam generation (HRSG) system 20 may be used forsecondary recovery of heat through generation of steam, which may alsobe used to drive generators 16 for generating electrical power. Inaddition to selling electricity to the grid 18, the energy system 10 mayalso buy electricity from the grid 18. Whether the energy system 10 buysfrom or sells to the grid 18 at any particular point in time depends onthe current electricity supply of the energy system 10, the currentelectricity demand of the energy system 10, electrical storage capacityof the energy system 10, buy/sell prices to and from the grid 18,day/night cycles of the energy system 10, the availability and capacityof other generation systems connected to the grid 18, and so forth.

As illustrated, the energy system 10 may include process units 22 andbuildings 24 that consume some of the electrical power, chilled water,and/or steam. In addition, in certain embodiments, the energy system 10may include electric chillers 26 and steam chillers 28, which may beassociated with a thermal energy storage tank 30, and may consume energyto generate chilled water, which may be pumped to the process units 22and buildings 24 by pumps 32 for cooling, such as for building cooling,industrial process cooling, and so forth. In addition, heated waterfrom, for example, the chillers 26, 28 may be circulated through acooling tower 34 and associated heat exchangers 36 and pumps 38, wherethe heated water is cooled for later use.

Therefore, in summary, various components may produce energy (i.e.,referred to as sources) and/or consume energy (i.e., referred to assinks) in a typical commercial or industrial energy system 10. Indeed,the components shown in FIG. 1 are merely exemplary of the componentsthat may comprise a typical commercial or industrial energy system 10.As illustrated in FIG. 1, the various components of the energy system 10may be configured to consume and/or produce energy based upon differenttechnologies. The interdependence of the components of the energy system10 may, in certain embodiments, be extremely complex. In addition,various external components, such as the electrical grid 18 may add tothe complexity of the energy system 10. Again, the energy system 10illustrated in FIG. 1 is merely exemplary of the types of complex plantsand enterprises that may utilize the graphical modeling frameworkdescribed herein.

FIG. 2 is a block diagram of exemplary components of the energy system10 of FIG. 1, illustrating various interconnections. In particular, FIG.2 depicts various energy loops that are typical in commercial andindustrial energy systems 10. For example, key energy loops include afuel loop 40, an electric loop 42, a condenser loop 44 (e.g., coolingtower water), an evaporator loop 46 (e.g., chiller water), and a steamloop 48. The various energy loops 40, 42, 44, 46, 48 illustrated in FIG.2 are merely exemplary and not intended to be limiting. In otherembodiments, other energy loops may be used to model the energy system10.

Each energy loop 40, 42, 44, 46, 48 includes a set of defining variablesthat function as inputs and outputs for the respective energy loop 40,42, 44, 46, 48. For example, the fuel loop 40 includes t^(G), p^(G),f^(G), and r, where t^(G) is the fuel temperature, p^(G) is the fuelpressure, f^(G) is the fuel flow rate, and r is the heat factor for thefuel loop 40. The electric loop 42 includes kw, which is the amount ofelectricity supplied. The condenser loop 44 includes ts_(C), tf_(C), andf^(C), where ts_(C) is the temperature of the water entering the coolingtower(s), tf_(C) is the temperature of the water exiting the coolingtower(s), and f^(C) is the flow rate for the water in the condenser loop44. The evaporator loop 46 includes ts_(E), tf_(E), and f^(E), wherets_(E) is the temperature of the chilled water leaving the chillers,tf_(E) is the temperature of the chilled water returning to thechillers, and f^(E) is the chilled water flow rate. The steam loop 48includes t^(S), p^(S), and f^(S), where t^(S) is the steam temperature,p^(S) is the steam pressure, and f^(S) is the steam flow. Again, all ofthe variables for the energy loops 40, 42, 44, 46, 48 illustrated inFIG. 2 are merely exemplary and not intended to be limiting. In otherembodiments, other variables may be used to define the energy loops 40,42, 44, 46, 48.

As illustrated, the energy loops 40, 42, 44, 46, 48 are coupled tocomponent blocks, which represent groups of actual energy-relatedequipment of the energy system 10 that typically supply energy to orconsume energy from the energy loops 40, 42, 44, 46, 48. For example, aboiler block 50 is coupled to both the fuel loop 40 and the steam loop48, an electrical generator block 52 is coupled to the fuel loop 40, theelectric loop 42, and the steam loop 48, an evaporation chiller block 54is coupled to the electric loop 42, the condenser loop 44, and theevaporator loop 46, and an absorption chiller block 56 is coupled to theevaporator loop 46 and the steam loop 48. Again, the various componentblocks 50, 52, 54, 56 illustrated in FIG. 2 are merely exemplary and notintended to be limiting. In other embodiments, other component blocksmay be coupled to the various energy loops 40, 42, 44, 46, 48.

The disclosed embodiments facilitate both planning/scheduling andcontrol/operation of the energy system 10 of FIGS. 1 and 2. Morespecifically, as described in greater detail below, the embodimentsdescribed herein include a graphical language and interface andtransparent modeling framework for the energy system 10 of FIGS. 1 and 2that enables different sets of distributed users having widely differentareas of expertise to interact with parametric hybrid models for theindividual component blocks (e.g., groups of equipment) of the energysystem 10. Indeed, it should be understood that while the embodimentsdescribed herein are presented as relating to energy-efficient operationof energy systems 10, in other embodiments, the graphical language andinterface and transparent modeling framework of the embodimentsdescribed herein may be extended to other applications, such as chemicalmanufacturing, oil and gas processing, and so forth.

The disclosed embodiments target optimization of the energy system 10 ofFIGS. 1 and 2 that addresses the computational complexity challenge ofmodeling the many various energy-related components of the energy system10, including individual parametric hybrid models for generation units,boilers, chillers, pumps and fans, and so forth, as well as parametrichybrid models for constraints and objectives. In addition, the disclosedembodiments provide for online modification of model structure and/orparameters by the different sets of distributed users via a graphicallanguage and interface and transparent modeling framework.

Parametric objective functions may be built to reflect the economicobjectives of the operation of the energy system 10. A parametricconstraint set may be built to reflect constraints of the operation ofthe energy system 10 (e.g. constraints on cooling capacity, constraintson allowable emissions, and so forth). As described in greater detailbelow, the graphical language described herein enables all stakeholdersin the energy system 10 to interact with the parameters of theparametric hybrid models, the parametric objective functions, and theparametric constraint sets, even if access to the underlying parametrichybrid models are limited to particular users (e.g., modeling experts).Energy load models may also be built to predict load profiles over anoperation time horizon. The load models may include, for example,chilled water demand, steam demand, electricity demand, and so forth.Based on all of these models and objectives, the optimization problemfor the energy system 10 may then be solved to determine the optimalprofile for the operating conditions of the energy system 10, subject tothe parametric constraint set.

Because of the complexity of typical commercial and industrial energysystems 10, the hybrid techniques described herein provide uniqueadvantages. Hybrid techniques leverage known fundamental relationships(e.g., known kinetic models, and so forth) that are more or lessavailable from fundamental process modeling with empirical modelingtechniques for phenomena not accurately modeled due to a lack offundamental understanding. Because industrial-scale energy equipment isgenerally uniquely designed and developed for intensive operations,significant calibration or tuning of published or available fundamentalmodeling with specifically-designed empirical modeling techniquesprovides more accurate energy models. In turn, a more accurate energymodel enables a more highly performing model-based optimization andcontrol solutions. Therefore, an ideal modeling solution incorporatesthe best available fundamental models and empirical models tuned orcalibrated to best match collected energy equipmentmeasurement/performance data over varying operating phases of the energysystem 10. Depending on the accuracy of the parametric hybrid models,either linear (e.g. single value) parameters or nonlinear (e.g. kineticparameters that vary with measured energy) variables may be identifiedand used.

FIG. 3 is a block diagram of an exemplary parametric hybrid model 58 formodeling the energy system 10 and/or, more particularly, individualcomponent blocks 50, 52, 54, 56 of the energy system 10. As illustrated,energy variable inputs u_(k) from the energy system 10 may be receivedby the parametric hybrid model 58. The energy variable inputs u_(k) may,for example, include the variables of the energy loops 40, 42, 44, 46,48 described above. An empirical model 60 may use the energy variableinputs u_(k) to generate empirical model outputs w_(k). The empiricalmodel outputs w_(k) may be a function of the energy variable inputsu_(k) and empirical model parameters ρ. Both the empirical model outputsw_(k) and the energy variable inputs u_(k) may be directed into aparameter model 62 of the parametric hybrid model 58. Fundamental modelparameters θ_(k) from the parameter model 62 may be a function of theenergy variable inputs u_(k) and the empirical model outputs w_(k). Itshould be noted that both the length of the fundamental model parametersθ_(k) and the value of the parameter vector may vary as a function ofthe energy variable inputs u_(k) and the empirical model outputs w_(k).In certain embodiments, the fundamental model parameters θ_(k) mayinclude the empirical model outputs w_(k), or may simply be identical tothe empirical model outputs w_(k) in their simplest form. Thefundamental model parameters θ_(k) may be directed into a parametricfirst-principles model 64, which may be either a steady-state or dynamicmodel. In addition, the parametric first-principles model 64 may receivethe energy variable inputs u_(k) from the energy system 10. Theparametric first-principles model 64 may model measured or unmeasuredenergy state variables x_(k) and energy variable outputs y_(k). Theenergy state variables x_(k) may be a function of the energy variableinputs u_(k), previous energy state variables x_(k), and the fundamentalmodel parameters θ_(k). The energy variable outputs y_(k) may be afunction of the energy variable inputs u_(k), current energy statevariables x_(k), and the fundamental model parameters θ_(k). The energyvariable outputs y_(k) may be directed from the parametric hybrid model58 as outputs. Therefore, the general equations defining the parametrichybrid model 58 include:w _(k) =f ₁(u _(k),ρ);θ_(k) =f ₂(u _(k) ,w _(k));x _(k) =F _(k)(u _(k) ,x _(k-1),θ_(k)); andy _(k) =G _(k)(u _(k) ,x _(k),θ_(k));

where u_(k) is a vector of energy variable inputs over time k, ρ is avector of empirical model parameters, w_(k) is a vector of empiricalmodel outputs over time k, θ_(k) is a vector of fundamental modelparameters over time k, x_(k) is a vector of measured or unmeasuredenergy state variables over time k, and y_(k) is a vector of energyvariable outputs over time k.

The parametric hybrid model 58 is extremely efficient for real-timeoptimization and control computations. This computational efficiency iscritical to the successful implementation of a model-based optimizationand control strategy that optimizes the performance of the energy system10. Dynamic optimization methods are used to calculate optimal dynamictrajectories during operation of the energy system 10 to optimize theefficiency of the energy system 10 as a whole. In particular,trajectories may be calculated for individual components of thecomponent blocks 50, 52, 54, 56 of the energy system 10 and optimized toa target over time based on parameters that are closely related to, butare not the same as, the input and output variables which are listedabove as being associated with the various energy loops 40, 42, 44, 46,48. More specifically, as illustrated in FIG. 3, the fundamental modelparameters θ_(k) generated by the parameter model 62 may be a set ofparameters that are not directly analogous to either the energy variableinputs u_(k) or the energy variable outputs y_(k). Rather, certainderived measures (e.g., the parameters) of the energy system 10 over thecourse of operation of the energy system 10 may be used to generatetrajectories that strongly correlate to performance variables for theenergy system 10, even when the performance variables for the energysystem 10 are not directly measurable.

For example, the efficiency of a boiler may not be measured duringoperation of the energy system 10, and may be used as a parameter, whichcorrelates to, but is not that same as, energy variable inputs andoutputs u_(k), y_(k) for the boiler component block 50. Therefore, thisparameter may be calculated during operation of the energy system 10(and, more specifically, the components of the boiler component block50) with the parametric hybrid models 58, and may be used in calculatingan optimal trajectory for an input to the boiler (e.g. the firing rateof the boiler). This allows better real-time control during operation ofthe energy system 10, such that intermediate performance of the energysystem 10 may be more closely targeted and maintained. In certainembodiments, an optimal trajectory function may be determined bysolving:min(u _(k))Γ(ŷ _(k) ,ŷ _(k) ^(Trajectory)), subject to:w _(k) =f(u _(k),ρ);θ_(k) =f(u _(k) ,w _(k));x _(k) =F _(k)(u _(k) ,x _(k-1),θ_(k));y _(k) =G _(k)(u _(k) ,x _(k),θ_(k)); andL<u _(k) <H;

where Γ( ) is the objective function defined over energy variableoutputs, ŷ_(k) is the energy variable outputs (ŷεy), and ŷ_(k)^(Trajectory) is an explicit or implicit representation of a desiredenergy variable trajectory. In addition, constraints (e.g., L and Habove) may be trajectory functions. The minimization of the aboveobjective function is achieved through adjustments to the decisionvariables u_(k) (e.g., the energy variable inputs). Note that theoptimization problem above is merely exemplary and not intended to belimiting. For example, the objective function Γ( ) may be defined toinclude penalties on decision variables u_(k).

The dynamic optimization described above may be implemented usingvarious methods. The level of detail included in the parametric hybridmodels 58 may vary depending upon the level of complexity that may behandled in real time. In other words, the parametric hybrid modelingallows a systematic way of compromising between model accuracy andcomputational complexity and, therefore, offers flexibility to handleenergy systems 10 of varying levels of complexity. More specifically,the complexity of any given parametric hybrid model 58 is a function ofboth the complexity of the system being modeled, and the simplicity ofthe parametric hybrid model 58 needed to make real-time computationstractable. As such, the parametric hybrid model framework offers asystematic framework for optimally trading off model accuracy versuscomputational efficiency. In defining parametric hybrid models 58, incertain embodiments, short-cut models may be used (e.g., in theparametric first-principles models 64). These short-cut models may belinear or nonlinear, dynamic or steady-state, and so forth. Theparametric hybrid model framework remains current with the real-timeoperating conditions of the energy system 10, and allows for onlinemodification of the model parameters, which are not direct inputs oroutputs of the energy system 10, and hence the decision engine (i.e.,the optimization and control) always has valid models upon which to basedecisions.

The parametric hybrid model 58 models both steady-state and thenon-steady-state behavior of the processes of the energy system 10,whether the behavior is linear or nonlinear, with respect to criticalvariables, where gains and/or dynamics vary during operation of theenergy system 10. The optimization problem formulation for optimizationand/or control of the energy system 10 has: (1) parametric hybrid models58 of the components of the energy system 10, (2) parametric hybridmodels 58 of how these components are connected together to define theenergy system 10, (3) a parametric hybrid description of what theperformance objectives are, and (4) a parametric hybrid description ofwhat the constraints are. It should be noted that a parametric hybridmodel/description may degenerate to a constant in simple cases. Some ofthe variables (e.g., the parameters described herein) that areindicative of performance of the energy system 10 (or individualcomponents of the energy system 10) may not be measured or even easilymeasurable. The parametric hybrid models 58 are used to model thesevariables (e.g., the parameters described herein) as well. Then, anoptimizer may make decisions as to which inputs to the energy system 10should be given system models/objectives/constraints. As such, theparametric hybrid model framework allows all of the models to remaincurrent, while solving the optimization problem (i.e., making decisions)as quickly as possible. Achieving these two goals enables the optimalenergy management system to continuously make the best decisions basedon what is actually happening with the energy system 10 in substantiallyreal-time during operation of the energy system 10.

As described above with respect to FIG. 2, each component block 50, 52,54, 56 may be associated with energy loops 40, 42, 44, 46, 48 thatcontribute to operation of the component block 50, 52, 54, 56. Inaddition, each component block 50, 52, 54, 56 will include actualenergy-related equipment components. Moreover, each component block 50,52, 54, 56 may be modeled by a parametric hybrid model 58 as describedabove with respect to FIG. 3. For example, FIG. 4 is a block diagram ofan exemplary evaporation chiller block 54 of FIG. 2. As illustrated, theevaporation chiller block 54 may include a condenser 66, a compressor68, an evaporator 70, and a valve 72. As such, the evaporation chillerblock 54 may be associated with the condenser loop 44 (e.g., thecondenser 66), the electric loop 42 (e.g., the compressor 68), and theevaporator loop 46 (e.g., the evaporator 70).

Accordingly, the variables of the condenser loop 44, the electric loop42, and the evaporator loop 46 will be associated with the evaporationchiller block 54. More specifically, the variables ts^(C), tf^(C),f^(C), kw, ts^(E), tf^(E), and f^(E) comprise input and output energyvariables u_(k), y_(k) for the evaporation chiller block 54. However, aparametric hybrid model 58 may be built that incorporates fundamentalmodels for the condenser 66, compressor 68, evaporator 70, and valve 72(e.g., in a parameter model 62), empirical data relating to thecondenser 66, compressor 68, evaporator 70, and valve 72 (e.g., in anempirical model 60), and a parametric first-principles model 64 for theevaporation chiller block 54. From this, the parametric hybrid model 58of the evaporation chiller block 54 will model critical parameters θ_(k)of the evaporation chiller block 54. These critical parameters θ_(k) aredifferent from the input and output energy variables u_(k), y_(k) forthe evaporation chiller block 54. However, they correlate withperformance criteria of the evaporation chiller block 54. For example,critical parameters of the evaporation chiller block 54 may includeentropy production and thermal resistance. These parameters correlatewell with, but are not equal to, the input and output energy variablesu_(k), y_(k) for the evaporation chiller block 54 (e.g., ts^(C), tf^(C),f^(C), kw, ts^(E), tf^(E), and f^(E)).

As another example, FIG. 5 is a block diagram of an exemplary boilerblock 50 of FIG. 2. As illustrated, the boiler block 50 may include afurnace 74, an economizer 76, and a steam drum 78. As such, the boilerblock 50 may be associated with the fuel loop 40 (e.g., the furnace 74and the economizer 76) and the steam loop 48 (e.g., the steam drum 78).Accordingly, the variables of the fuel loop 40 and the steam loop 48will be associated with the boiler block 50. More specifically, thevariables t^(G), p^(G), f^(G), r, t^(S), p^(S), and f^(S) comprise inputand output energy variables u_(k), y_(k) for the boiler block 50.However, a parametric hybrid model 58 may be built that incorporatesfundamental models for the furnace 74, economizer 76, and steam drum 78(e.g., in a parameter model 62), empirical data relating to the furnace74, economizer 76, and steam drum 78 (e.g., in an empirical model 60),and a parametric first-principles model 64 for the boiler block 50. Fromthis, the parametric hybrid model 58 of the boiler block 50 may generatemodels for critical parameters θ_(k) of the boiler block 50. Thesecritical parameters θ_(k) are different from the input and output energyvariables u_(k), y_(k) for the boiler block 50. However, they correlatewith performance criteria of the boiler block 54. For example, criticalparameters of the boiler block 50 may include the efficiency of thefurnace. This parameter correlates well with, but is not equal to, theinput and output energy variables u_(k), y_(k) for the boiler block 50(e.g., t^(G), p^(G), f^(G), r, t^(S), p^(S), and f^(S)).

Therefore, parametric hybrid models 58 can be built for variouscomponent blocks 50, 52, 54, 56 of the energy system 10. Components ofthe component blocks 50, 52, 54, 56 may include power generation units,such as gas turbines, wind turbines, solar panels, and so forth. Asdescribed above, an electricity grid 18 may also be considered as apower generation source, and may be modeled using the parametric hybridmodels 58. Other components of the component blocks 50, 52, 54, 56 thatmay be modeled include chillers (e.g., such as illustrated in FIG. 4),boilers (e.g., such as illustrated in FIG. 5), cooling towers, pumps,fans, motors, thermal storage units, and so forth. In addition,parametric hybrid models 58 may be developed for loads, such as steamloads, chilled water loads, electricity loads, and so forth.Furthermore, other parametric hybrid models 58 may be developed forvarious power generation sources and power consumption components. Inaddition, not only may parametric hybrid models 58 be developed forcomponent blocks 50, 52, 54, 56, such as those illustrated in FIG. 2,but parametric hybrid models 58 of the interconnections (e.g., theenergy loops 40, 42, 44, 46, 48) between the components may also bedeveloped.

The parametric hybrid models 58 will capture the performance andeconomics of the operation of the energy system 10, operationalconstraints of the energy system 10, existing knowledge regardingoperation of the energy system 10, and objectives for the operation ofthe energy system 10. The optimal operating conditions of the energysystem 10 may be determined via a systematic optimization problem usingan appropriate solver (e.g., an algorithmic search for the bestsolution). However, in other embodiments, the optimal operatingconditions of the energy system 10 may be determined using heuristicsearches, rule-based reasoning, fuzzy logic, and so forth. Anotheraspect of the disclosed embodiments is the ability to modify theparameters of the parametric hybrid models 58 defining the energy system10 based on updated data regarding new operating conditions of theenergy system 10.

Various embodiments of systems and methods for applying parametrichybrid models 58 are described below. In this approach, the parametrichybrid models 58 that define the energy system 10 may be incorporated asan integrated model in a parametric hybrid model-based systemmanager/controller. This system may project or predict what will happenin the energy system 10 based on the integrated parametric hybrid model58 and recent historical data including, for example, recent operatingconditions and/or state values, and predictions of weather/load that maybe obtained from many resources, including other parametric hybridmodels 58, among other things. This projection or prediction may beupdated or biased based on received current information, specifiedobjectives, and/or constraints of the energy system 10. Optimizationalgorithms may be used to estimate the best current and future controladjustments on the model inputs to achieve a desired response of theenergy system 10. Targets are set and the integrated parametric hybridmodel outputs may be compared to how that output behaves in order tomaintain the desired accuracy of the integrated parametric hybrid models58.

As described above, parametric hybrid models 58 may be developed for anyof the component blocks of a system (e.g., the component blocks 50, 52,54, 56 of the energy system 10 described above). In addition, theparametric hybrid models 58 may be linked together to form networks ofparametric hybrid models 58 that interact with each other in aplant-wide or enterprise-wide manner. As such, not only do theindividual parametric hybrid models 58 model complex operation forindividual component blocks of the system 10, but the interactionsbetween the individual parametric hybrid models 58 form networks havingcomplex data flows and constraints between the parametric hybrid models58.

A graphical modeling tool may be used to define relationships and dataflows between parametric hybrid models 58. More specifically, thegraphical modeling tool may be configured to represent relationshipsbetween components of a system (e.g., spatial relationships between thecomponents, fluid flows between the components, product flows betweenthe components, power flows between the components, and so forth),wherein the components that are represented by the graphical modelingtool are modeled using the parametric hybrid models 58. For example,FIG. 6 is an example of a graphical user interface 80 (i.e., a graphicalrepresentation) of the graphical modeling tool 82 representing aplurality of parametric hybrid models 58 relating to components of thesystem 10 arranged as a network 84. In particular, in the illustratedexample, the system 10 includes a power grid component block 86 (i.e.,P.0), which functions as a power source for four chiller componentblocks 88 (i.e., EC.0, EC.1, EC.2, and EC.3), and a chilled watercomponent block 90 (i.e., CW.0), which functions as a sink for the fourchiller component blocks 88. Each of the component blocks 86, 88, 90 ismodeled as a parametric hybrid model 58 as described above, and isgraphically represented as a node 92 that may be connected to the othernodes 92 (i.e., the other component blocks 86, 88, 90) via connections94, which is also modeled as a parametric hybrid model.

Each of the nodes 92 relating to the component blocks 86, 88, 90 andconnections 94 for the component blocks 86, 88, 90 are defined such thatthe exemplary network 84 in FIG. 6 unambiguously defines a well-posedoptimization problem. As such, each of the nodes 92 and connections 94are characterized by decision variables and parameters in theoptimization problem. Therefore, in the graphical representation of theoptimization problem, the nodes 92 capture how decision variablesinfluence the objective functions. This distinguishes the graphicalrepresentation of the optimization problem (exemplified in network 84)from the graphical representations commonly used to simulate a process,as the connections between nodes in a simulation scenario reflect thephysical impact of one node's output as input to another node. Thesemore common input and output flows to and from the nodes 92 in thenetwork 84 (such as the ones needed for simulating a process) arecompletely abstracted from the decision variables. Therefore, each ofthe connections 94 includes a direct translation into the optimizationproblem that is constructed and maintained by the graphical language.This allows the parametric hybrids models 58 and the connections 94between the parametric hybrid models 58 to be developed by modelingexperts, but the graphical components illustrated in FIG. 6 to beviewable by any users of the system 10 that have access to the graphicalmodeling tool 82 and are authorized to view and/or modify the parametrichybrid models 58 relating to the graphical components.

FIG. 7 is a block diagram of an enterprise-integrated parametric hybridmodel enabled control/optimization system 96 for controlling andoptimizing the system 10 of FIG. 1. As described in greater detailbelow, the control/optimization system 96 includes the graphicalmodeling tool 82, which enables the graphical user interface 80illustrated in FIG. 6 to be displayed to users of thecontrol/optimization system 96. More specifically, users who have accessto the control/optimization system 96 may display the graphical userinterface 80 on any compatible electronic devices to interact withparametric hybrid models 58 representing components of the system 10. Asillustrated in FIG. 7, the control/optimization system 96 is directlyconnected to the system 10. More specifically, in certain embodiments,the control system 96 may include a plurality of sensors 98 andactuators 100 that are connected to individual components 102 (i.e.,physical equipment) of the system 10. Generally speaking, the sensors 98are configured to receive signals relating to operating information ofthe components 102 of the system 10, and the actuators 100 areconfigured to receive signals transmitted by the control system 96 forcontrolling operation (i.e., valve settings, pump and compressor speeds,and so forth) of the components 102.

As such, the control/optimization system 96 is a computer system forcontrolling operation of the system 10. The control/optimization system96 may include any of various types of computer systems or networks ofcomputer systems, which execute software programs 104 according tovarious embodiments described herein. The software programs 104 mayperform various aspects of modeling, prediction, optimization, and/orcontrol of the system 10. The control/optimization system 96 may furtherprovide an environment for making optimal decisions using anoptimization solver and carrying out those decisions (e.g., to controlthe system 10). In particular, the control/optimization system 96 mayimplement parametric hybrid model control of the system 10. Morespecifically, the parametric hybrid models 58 relating to the components102 of the system 10 may be utilized to enable the parametric hybridmodel control of the system 10.

In addition, the control/optimization system 96 is configured togenerate and transmit the graphical user interface 80 depicted in FIG. 6to remote users 106 of the control/optimization system 96. Morespecifically, the control/optimization system 96 is configured totransmit graphical user interfaces 80 across a communication network 108to electronic devices 110 that may be located remotely from the system10. For example, in certain embodiments, the communication network 108may include a local area network (LAN). However, the communicationnetwork 108 may also include the Internet, with the control/optimizationsystem 96 functioning as a server to generate and transmit the graphicaluser interfaces 80 to electronic devices 110 located anywhere. Theelectronic devices 110 may be desktop computers, laptops computers,smart phones, or any other electronic devices capable of displaying thegraphical user interfaces 80 on a display 112 of the electronic device110, and capable of receiving inputs from the user 106 of the electronicdevice 110 via interfaces 114 of the electronic device 110. Thecontrol/optimization system 96 is designed such that potentiallyasynchronous inputs from local or remote users 106 are alwaysincorporated into the online model after proper integrity checks by theparametric hybrid models 58. These integrity checks are embedded withinthe parametric hybrid models 58 when these models are defined.

The control/optimization system 96 includes a non-transitory memorymedium 116 on which the software programs 104, data relating to theparametric hybrid models 58, operating data (both real-time andhistorical) for the system 10, and so forth, are stored. The term“memory medium” is intended to include various types of memory orstorage, including an installation medium (e.g., a CD-ROM, or floppydisks), a computer system memory or random access memory such as DRAM,SRAM, EDO RAM, RAMBUS RAM, and so forth, or a non-volatile memory suchas a magnetic medium (e.g., a hard drive), or optical storage. Thememory medium 116 may comprise other types of memory as well, orcombinations thereof. A processor 118 executing code and data from thememory medium 116 comprises a means for creating and executing thesoftware programs 104 according to the methods described herein. Thecontrol/optimization system 96 may take various forms, including apersonal computer system, mainframe computer system, workstation,network appliance, Internet appliance, or other device. In general, theterm “computer system” can be broadly defined to encompass any device(or collection of devices) having the processor 118 (or processors),which executes instructions from the memory medium 116 (or memorymedia).

The users 106 of the control/optimization system 96 may have varyingsecurity access levels, which may be determined when the users 106 enterlogin credentials into the electronic devices 110, or may be determinedusing other methods, such as having access rights stored on theelectronic devices 110, and so forth. For example, as illustrated inFIG. 7, the users 106 of the control/optimization system 96 may includemanager-level users 120 and engineer-level users 122 (e.g., plantengineers or operators). As described in greater detail below, themanager-level users 120 may have access to only a subset of the features(e.g., command inputs) available to the engineer-level users 122. Forexample, the manager-level users 120 may be allowed to modifyoptimization constraints of the parametric hybrid models 58 representingthe components 102 of the system 10, whereas the engineer-level users122 may be allowed to modify optimization constraints of the parametrichybrid models 58 as well as also modifying the underlying parametrichybrid models 58. As such, the command inputs that are enabled in thegraphical user interfaces 80 transmitted to the users 106 will varydepending on the security access levels of the particular users 106.

When a user 106 submits a command input (e.g., clicking on a node 92 orconnection 94 to interact with the node 92 or connection 94), otherusers 106 of the control system 96 will be notified of the command inputin substantially real-time (e.g., during operation of the system 10). Inother words, the command input will be transmitted from the electronicdevice 110 being used by the user 106 to the control/optimization system96, and the effect of the processed command input will be pushed out(i.e., broadcast) to other electronic devices 110 being used by otherusers 106. As such, the interactions that occur with the parametrichybrid models 58 will be transparent to all users 106 of the controlsystem 96. The users 106 may also interact with the control/optimizationsystem 96 in a sand-box mode where all the changes are understood to belocal to the particular user 106 and have no impact on the onlineapplication. This sand-box mode allows each user 106 to perform what-ifanalysis, for example, using the most current state of the system 10without interfering with the online application. While the simulatedwhat-if scenarios may be recorded locally (e.g., on the electronicdevice 110), in certain embodiments, any commitment of changes to thecontrol/optimization system 96 may be subject to an authorizationprocess. For example, an engineer-level user 122 may have to approve thewhat-if scenarios before they are committed.

Furthermore, each model is deployed as a server service that can servemultiple requests to multiple electronic devices 110. This enables allusers 106 to investigate the functioning of the parametric hybrid models58 during operation of the system 10. More specifically, as the model isdeployed and running, each node 92 (e.g., the component blocks relatingto components 102 of the system 10) is capable of providing informationto the users 106 via the graphical user interfaces 80. As such, theusers 106 are able to view data relating to accuracy of the modelsduring operation of the system 10. In addition, the same deployed modelwill be capable of providing other services, such as being used forcalculating key performance indicators at the same time that it is beingutilized by the control/optimization system 96.

As described above, model validation has conventionally been viewed asan offline activity. However, the embodiments described herein embed thelogic for data filtering and the algorithms for parameter identification(e.g., as a closed-form solution) and optimization as properties of thedeployed parametric hybrid models 58 and create the model qualitymeasure as a parameter of the parametric hybrid models 58. Morespecifically, again, the graphical modeling tool 82 functions as aserver service, allowing the deployed online model (i.e., the network 84of parametric hybrid models 58) to avoid performance degradation whenthe model quality measure is calculated. In certain embodiments, themodel quality is mapped to model parameters, such that model qualityinformation is made available to the users 106 of the control system 96.For example, using the parametric hybrid models 58, model error may beeasily associated with model parameters (e.g., by defining acceptableranges for the parameters), and the users 106 may take specific actionsin response to model quality deterioration.

The deployment strategy for the transparent parametric hybrid models 58enables distributed and asynchronous validation and modification of thedeployed model. This is particularly advantageous inasmuch as thecomponents of the model are distributed throughout the plant and/orenterprise. In addition, the transparency is two-way. In other words,while model quality is accessible to any authorized user 106 of thecontrol system 96, any modification by any authorized user 106 istransparent to all authorized users 106. Furthermore, the parametricnature of the model enables graphical representation of the modelquality (e.g., bounds on model parameters, where the current value ofthe parameter falls within the bounds, and so forth).

Because the transparent parametric hybrid models 58 are composed ofpotentially distributed components 102 with corresponding owners andstakeholders of the components 102, the integrity of the deployed modelis ensured through efficient ownership modeling. For example, modelownership (e.g., of specific parametric hybrid models 58, and so forth)is an intrinsic property of the deployed model. The ownership propertyfor specific parametric hybrid models 58 is used as a key by whichaccess and modification of the parametric hybrid models 58 may beauthenticated and implemented. In other words, if the user 106 is not anowner of a particular parametric hybrid model 58, or does not havesufficient access rights to the parametric hybrid model 58, the user 106may be prevented from interacting with the parametric hybrid model 58.In other words, the graphical user interface 80 presented to the user106 via the electronic device 110 only presents the user 106 withactions (i.e., command inputs) to which the user 106 has access. Theownership property applies to both nodes 92 and connections 94 of themodel network 84 for the plant and/or enterprise and, therefore, theownership properties are used for validation of any graphicalmanipulation of the parametric hybrid models 58 (e.g., addition anddeletion of parametric hybrid models 58 to and from the model network84).

In addition, certain graphical manipulations (i.e., command inputs) ofthe parametric hybrid models 58 performed by certain users 106 may besubject to approval by other users 106 before being implemented. Forexample, in certain embodiments, command inputs performed bymanager-level users 120 may be subject to approval by engineer-levelusers 122 before being implemented. This approval mechanism is enabledby the transparent nature of the graphical modeling tool 82 inasmuch ascommand inputs performed by any users 106 of the control system 96 arepushed to the graphical user interfaces 80 of other devices 110connected to the control system 96 in substantially real-time.

For example, returning now to FIG. 6, the users 106 of the graphicalmodeling tool 82 need only interact with the graphical information viathe graphical user interface 80. For example, if the user 106 wishes toadd or modify a constraint of the system 10, the user 106 need onlyclick on a node 92 or connection 94, which brings up a dialog box thatenables the user 106 to add the constraint information. In addition, theusers 106 of the graphical modeling tool 82 may add and/or deletecomponent blocks from the graphical user interface 80. In other words,the component blocks represented in any given network 84 via thegraphical user interface 80 need not represent all of the physicalcomponents 102 of the actual system 10 that is being modeled andoptimized. Rather, the user 106 may only be interested in (or haveaccess to) certain sets of the physical components 102 of the system 10.As such, the user 106 may personalize the graphical user interface 80 toinclude component blocks of interest to the user 106.

For example, FIG. 8 is an example of the graphical user interface 80(i.e., a graphical representation) of the graphical modeling tool 82illustrating a library 124 of component blocks available to the user 106to be added to the graphical user interface 80. For example, the user106 may drag-and-drop any of the component blocks listed in the library124 into the graphical user interface 80. In certain embodiments, thegraphical modeling tool 82 will automatically create and/or remove theappropriate connections 94 between component blocks (i.e., the nodes 92)that are added and/or deleted by the user 106 via the graphical userinterface 80 being viewed by the user 106. In addition, it will beunderstood that the settings of the personalized graphical userinterfaces 80 created by the users 106 may be saved and re-opened asneeded.

As such, any particular graphical representation of the system 10 mayconvey different information to the user 106 depending on the context inwhich the graphical representation is involved. For example, if a Modeltab 126 of the graphical modeling tool 82 is selected by the user 106,and a connection 94 between one of the four chiller component blocks 88(i.e., EC.0, EC.1, EC.2, and EC.3) and the chilled water component block90 is clicked, a dialog box may be initiated, displaying the flow rate,temperature, and pressure of the chilled water leaving the chillercomponent block 88, for example. However, if an Network tab 128 of thegraphical modeling tool 82 is selected by the user (assuming the userhas access to the Network tab 128), and the connection 94 between thechiller component block 88 and the chilled water component block 90 isclicked, a dialog box may be initiated, displaying the chilled watertonnage produced by the chiller component block 88, for example.

In other words, the decision variables or constraints (e.g., parameters)of the parametric hybrid models 58 representing the component blocks areaccessible to users 106 when the Network tab 128 is selected (i.e., whenin Network mode). However, the actual physical inputs and outputs thatdescribe the particular equipment are not displayed when the Network tab128 is selected (i.e., when in Network mode). Rather, the actualphysical inputs and outputs that describe the particular equipment areonly displayed to the user when the Model tab 126 is selected (i.e.,when in Modeling or Operation mode). As such, in certain embodiments,only the users 106 (e.g., the engineer-level users 122) having thein-depth knowledge of the parametric hybrid models 58 representing thecomponent blocks may have access to the Model tab 126. Therefore, onlythese users 106 will be capable of interacting with the actual physicalinputs and outputs of the particular equipment. Conversely, any users106 of the system 10 that have access to the Network tab 128 may becapable of interacting with the decision variables of constraints of thesystem 10 for the purpose of performing optimization and control of thesystem 10.

Each node 92 in a network 84 can represent an objective function foroptimization and control of the system 10. This can be particularlybeneficial if multiple operational objectives are to be handledgraphically via the graphical user interface 80. Various objectives maybe capable of being interacted with via the graphical user interface 80and, as such, the user 106 may graphically modify the optimizationproblem for the system 10. For example, in certain embodiments, thegraphical modeling tool 82 may present the user 106 with a range ofvalues within which an optimization constraint for a particularparametric hybrid model 58 may be modified. In other words, withoutrequiring approval by engineer-level users 122, the graphical userinterface 80 may allow a manager-level user 120 to modify anoptimization constraint within a bounded range of feasible values forcontrol of the system 10.

Any and all command inputs submitted by the users 106 may redefine theoptimization objectives for the system 10. For example, a chillernetwork (e.g., the network 84 illustrated in FIGS. 6 and 8) receivingelectric energy and producing chilled water may be optimized to producea chilled water load with minimal energy use, or to maximize the chilledwater production given a maximum available electric energy, throughcommand inputs submitted via the graphical user interface 80 by the user106. For example, when an Optimization tab 130 is selected, the user 106may interact with optimization constraints of the network 84.

For example, FIG. 9 is an example of the graphical user interface 80(i.e., a graphical representation) of the graphical modeling tool 82illustrating an optimization view 132 when the Optimization tab 130 isselected by the user 106. More specifically, with the Optimization tab130 selected, FIG. 9 illustrates when the user 106 clicks the chilledwater component block 90. As such, the optimization view 132 depicted inFIG. 9 illustrates a time series 134 of the projected chilled waterdemand of the chilled water component block 90. In addition, theoptimization view 132 for the chilled water component block 90 includestime schedules 136 for each of the four chiller component blocks 88 thatare connected to the chilled water component block 90. Morespecifically, the time schedules depict when each of the chillercomponent blocks 88 are scheduled to be operative to achieve theprojected chilled water demand of the chilled water component block 90.

Assuming the user 106 is authorized to interact with the chilled watercomponent block 90, the user 106 may modify an optimization constraintof the chilled water component block 90 via the optimization view 132 ofthe graphical user interface 80. For example, FIG. 10 is an example ofthe graphical user interface 80 (i.e., a graphical representation) ofthe graphical modeling tool 82 illustrating the optimization view 132when the user 106 has submitted a command input (i.e., modified anoptimization constraint) and the optimization solution of the system 10has been updated. More specifically, in the example depicted in FIG. 10,the user 106 has modified the time series 134 of the projected chilledwater demand of the chilled water component block 90, and the timeschedules 136 of the four chiller component blocks 88 have been updated.In particular, the model of the control system 96 has updated theoptimization problem of the system 10 to determine that chillercomponent block EC.0 should be turned off between 16:00 and 18:00 andthat chiller component block EC.2 should be turned on between 16:00 and18:00. As illustrated in FIG. 10, a cost of the committed modificationis presented to the user 106 (e.g., at the bottom of the graphical userinterface 80). In certain embodiments, the cost of introducing theoptimization constraints may be reported to all users 106, and recordedin appropriate formats (e.g., in a database residing within thecontrol/optimization system 96, for example). This type of modificationof optimization constraints may be performed for any of the componentblocks (i.e., parametric hybrid models 58) of the network 84 displayedby the graphical user interface 80. Due to the global optimizationstrategy in the control/optimization system 96, the cost of respectingnewly defined constraints by the user 106 is calculated and shownimmediately to the user as shown in FIG. 10. The ability to graphicallyvary the load profile (e.g., time series 134) and immediately see thecosts/savings under various load profiles is a unique capability enabledby the graphical language for optimization presented herein.

The components blocks are parametric hybrid models 58 and, as such, arenot linear models in general (even though linear models are degenerateforms of parametric hybrid models 58). Therefore, the networks 84comprised of the parametric hybrid models 58 are similarly not going tobe linear optimization problems. Accordingly, when a user 106 modifiesan optimization constraint of a parametric hybrid model 58, thedetermination of a well-posed modified optimization problem is somewhatcomplex. A preferred method for determining the modified optimizationproblem for the graphical optimization language is to use a data-drivenconvex approximation over a trajectory for each parametric hybrid model58 in the network model 84. By definition, a function ƒ is convex if:ƒ(λx+(1−λ)y)≦λƒ(x)+(1−λ)ƒ(y),∀x,yεD _(ƒ),∀λε[0,1]

Furthermore, if ƒ and g are convex functions, then so is:αƒ+βg,∀α,β≧0

As a result, the overall model representing the network model 84 will beconvex. Any local minimum of a convex function is also a global minimum.Non-convex optimization problems benefit from tight, convexunderestimators. Assuming that ƒ is a twice differentiable function,then ƒ is convex if and only if:∇²ƒ(x)

0,∀xεD _(ƒ)

In the graphical representation of the optimization problem (e.g. thenetwork model 84 shown in FIG. 8), each node 92 exposes decisionvariables for the optimization problem. Each connection 94 determineshow decision variables in two nodes 92 are related (e.g., constrained).Therefore, the graphical presentation has a direct translation into theoptimization problem statement. Network topology, and any modificationto the network topology via graphical interactions with the network 84(e.g. adding a node 92, removing a connection 94), can be captured bylinear matrix operations. Therefore, a graphical representation of theoptimization problem will translate into a well-posed optimizationproblem if each component in the network 84 is approximated with aconvex function. A preferred method for this convexification in thegraphical language disclosed herein is to use automatic data-drivenconvex approximation of the network components along a predictedoperation trajectory. The parametric hybrid modeling paradigm allows forthis convex approximation with desired degrees of accuracy. Therefore,the optimization problem for the model of the system 10 may be solvedusing convex approximation where successive convexification of feasibleregions may be performed, with iteration confined to the feasibleregions. For example, FIG. 11 is an example of a non-linear andnon-convex function 138 of two variables 140, 142 relative to eachother. As illustrated, two convex approximations 144, 146 provide convexunderapproximators with different accuracies.

In addition, in certain embodiments, the solution to the optimizationproblem 138 is not ascertained in a deterministic manner. In otherwords, the optimization solution is not determined independent of thepoint at which the determination is begun. Rather, the optimizationsolution may be determined with the previous optimization solution inmind. For example, returning to the example of the modification of theoptimization constraint described with respect to FIG. 10, the updatingof the optimization solution between 16:00 and 18:00 begins under theassumption that operating chiller component block EC.0, chillercomponent block EC.1, and chiller component block EC.3 during this timeperiod is the optimal solution. As such, the modified optimizationsolution merely changes the scheduling times 136 such that chillercomponent block EC.2 instead of chiller component block EC.0 is operatedduring this time period. In other words, the model attempts to reach anoptimization solution as close to the previous optimization solution aspossible (i.e., in a non-deterministic manner).

As an example, the scheduling problem formulation may be defined by thefollowing functions:

${{\min{\sum\limits_{i \in M}{\beta_{i}f_{i}}}} + {\sum\limits_{i \in M}{\sigma_{i}g_{i}}} + {\sum\limits_{j \in N}{\kappa_{j}r_{j}}}},$such thatp _(i)=Γ_(i)(A _(i) p,B _(i) r,φ _(i)) ∀iεMƒ+Hp−g=0Zp≧δμ_(i) y _(i) ≦p _(i)≦ξ_(i) y _(i) ∀iεMy _(i)ε{0,1} ∀iεM

where M is the set of unit operations, N is the set of inputs, β, σ, andκ are costs associated with import of a product, sale of a product, andpurchase of a resource, respectively, r is a given resource input, p isproduct generated by a specific unit operation, A and B restrict unitoperation models, Γ, to a subset of products and inputs, φ is the set offitting parameters for a given model, μ and ξ are unit operation bounds,H, ƒ, and g allow product import and export, Z and δ set demandrequirements, and y is a binary variable for unit status. The linearnetwork model constraints H and Z may be defined by the user 106 (e.g.,by clicking on a parametric hybrid model 58 via the graphical userinterface 80). In addition, the discrete (or binary) decision variablesy_(i) may also be defined by the user 106. Furthermore, the constraintparameters δ, ƒ, g, β, σ, and κ may also be defined by the user 106.

FIG. 12 is an example of a solution graph 148 for the optimizationsolution equations described above. The solution graph 148 may bereferred to as a directed tree D=(V, E), where V is the set of unitoperation models, products, and resources V=(Γ, p, r, f, g) and E is theset of connections E=(H, Z). In general, the set of unit operationmodels V is analogous to the parametric hybrid models 58 (i.e., thenodes 92 of the model network 84) and E is analogous to the connections94 of the model network 84. FIG. 12 clearly demonstrates that withnonlinear unit operation models, Γ, the well-posedness of theoptimization problem is not trivial, and graphical manipulation of thesolution graph is not easily manageable. Successive data-drivenconvexification is the preferred approach to render such solution graphgraphically manageable.

FIG. 13 is an example of a method 150 for utilizing the graphical userinterface 80 to interact with the parametric hybrid models 58 describedherein. In step 152, an access level of a user 106 may be determinedwhen the user 106 enters login credentials into a remote electronicdevice 110, or may be determined using other methods, such as havingaccess rights stored on the electronic device 110, and so forth. Forexample, as described above, when the user 106 logs into the electronicdevice 110, the graphical modeling tool 82 may determine that the user106 is a manager-level user 120 or an engineer-level user 122. However,other access levels may be used, which may enable a more granular levelof authorization and functionality.

In step 154, the graphical user interface 80 is made available from thegraphical modeling tool 82 of the control/optimization system 96 to theelectronic device 110. The graphical user interface 80 enables aplurality of command inputs relating to the parametric hybrid models 58(i.e., which relate to actual physical components of a plant and/orenterprise) of a model network 84, and corresponding to the access levelof the user 106. For example, assuming the user 106 has appropriateaccess rights to a particular parametric hybrid model 58, a commandinput for modifying an optimization constraint (e.g., a predicted loadprofile) for the parametric hybrid model 58 may be enabled via thegraphical user interface 80. In addition, again assuming the user 106has appropriate access rights to the particular parametric hybrid model58, a command input for modifying how the parametric hybrid model 58functions (e.g., the inputs, outputs, parameters, and so forth, of theparametric hybrid model 58) may be enabled via the graphical userinterface 80.

Furthermore, as described in greater detail above, the graphical userinterface 80 enables the display of a plurality of parametric hybridmodels 58 represented as nodes 92 of a model network 84, and a pluralityof inputs and outputs of the plurality of parametric hybrid models 58represented as connections 94 between the nodes 92 of the model network84. The graphical user interface 80 enables the user 106 to add ordelete nodes 92 and connections 94 from the model network 84 to create apersonalized display of the parametric hybrid models 58 with which theuser 106 is authorized to interact.

In step 156, a command input is received from the graphical userinterface 80 by the graphical modeling tool 82 of thecontrol/optimization system 96. As described above, in certainembodiments, the command input may be transmitted (i.e., broadcast) toother users 106 of the control system 96 via other electronic devices110. Then, in step 158, the command input is processed by the graphicalmodeling tool 82 according to the access level of the user 106submitting the command input. For example, in certain embodiments, amodel quality of one or more of the parametric hybrid models 58 may bedetermined during operation of the system 10. As described above, theability to interrogate model quality during operation of the system 10is due to the transparent nature of the graphical modeling tool 82. Inaddition, in certain embodiments, the optimization problem of the modelnetwork 84 may be automatically re-adjusted by the control system 96during operation of the system 10, assuming the user 106 hasauthorization to make such a request, and that the request is feasible.However, in certain embodiments, the command input may also be subjectto approval by an engineer-level user 122, subject to boundingconstraints (e.g., only changes within specific ranges may be allowed),and so forth, prior to execution by the control system 96.

Regardless, the command inputs may all be used to modify control of thesystem 10 during operation of the system 10 via the control/optimizationsystem 96. For example, using the example described above with respectto FIG. 10, if a user modifies an optimization constraint of one of theparametric hybrid models 58, and the modification is found to befeasible by the control system 96 (i.e., via the graphical modeling tool82), then the resulting optimization solution may be automaticallyimplemented by the control/optimization system 96. For example,actuators 100 of the components 102 of the system 10 may be actuated inaccordance to the revised optimization solution. Again, using theexample described with respect to FIG. 10, the control system 96 mayautomatically control the system 10 to shut down chiller component blockEC.0 between 16:00 and 18:00, and to start up chiller component blockEC.2 between 16:00 and 18:00.

While only certain features of the invention have been illustrated anddescribed herein, many modifications and changes will occur to thoseskilled in the art. It is, therefore, to be understood that the appendedclaims are intended to cover all such modifications and changes as fallwithin the true spirit of the invention.

The invention claimed is:
 1. An enterprise-integrated system optimizer,comprising a non-transitory computer-readable medium having computerinstructions encoded thereon, wherein the computer instructions compriseinstructions for: transmitting a graphical representation of awell-posed optimization problem comprising an interconnected network ofnodes and connections to a remote electronic device, wherein theoptimization problem relates to an enterprise-integrated system andcomprises a parametric objective function, one or more decisionvariables, and one or more parametric constraints, wherein at least oneof the nodes comprises a parametric hybrid model whose output is adecision variable in the parametric objective function, and at least oneconnection comprises a parametric hybrid model that describes parametricconstraints on at least one decision variable in the optimizationproblem, and the graphical representation of the optimization problemenables graphically modifying at least one of the parametric objectivefunction, a decision variable, and a parametric constraint of theoptimization problem by interaction with a node or a connection, whereinthe parametric hybrid model for a node or connection comprises: anempirical model configured to generate a parameter model input based atleast in part on a variable input to the node or connection; a parametermodel configured to generate a fundamental model parameter based atleast in part on the parameter model input and the variable input; and aparametric first principles model configured to generate a variableoutput of the node or connection based at least in part on the variableinput and the fundamental model parameter; receiving a user commandinput relating to a node or connection in the graphical representationof the optimization problem; processing the user command input, whereinprocessing the user command input comprises modifying the optimizationproblem; and executing the optimizer with the modified optimizationproblem to determine current and future values of decision variables toimplement in the enterprise-integrated system.
 2. The system optimizerof claim 1, wherein executing the optimizer with the modifiedoptimization problem comprises modifying at least one decision variableto adjust operation of the enterprise-integrated system.
 3. The systemoptimizer of claim 1, wherein receiving the user command input comprisesreceiving a user command input to add or delete a node or connection. 4.The system optimizer of claim 1, wherein modifying the optimizationproblem comprises modifying a node parametric hybrid model, a connectionparametric hybrid model, or both, wherein the node parametric hybridmodel is configured to model a component in the enterprise-integratedsystem and the connection parametric hybrid model is configured to modela constraint in the optimization problem.
 5. The system optimizer ofclaim 1, wherein the computer instructions comprise instructions fordetermining an access level of a user, wherein receiving the usercommand input comprises receiving a user command input from anengineer-level user of the enterprise, and processing the user commandinput comprises modifying one or more parametric hybrid models.
 6. Thesystem optimizer of claim 1, wherein the computer instructions compriseinstructions for determining an access level of a user, whereinreceiving the user command input comprises receiving a user commandinput from a manager-level user of the enterprise, and processing theuser command input comprises modifying a constraint of the optimizationproblem.
 7. The system optimizer of claim 1, wherein the computerinstructions comprise instructions for determining an access level of auser, and receiving approval of modifications to the optimizationproblem committed by the user from another appropriately authorizeduser.
 8. The system optimizer of claim 1, wherein executing theoptimizer with the modified optimization problem comprises automaticallymodifying at least one parametric hybrid model along a predictedoperation trajectory of the enterprise-integrated system modifying theoptimization problem to adjust operation of the enterprise-integratedsystem, wherein a cost of adjusting operation is presented.
 9. Thesystem optimizer of claim 1, wherein executing the optimizer with themodified optimization problem comprises determining an optimizationsolution based at least in part on a previous optimization solution. 10.The system optimizer of claim 1, wherein modifying the optimizationproblem comprises convexification of one or more of the parametrichybrid models along a predicted operation trajectory of theenterprise-integrated system, wherein the parameter model, theparametric first-principles model, or both are convexified around thepredicted operation trajectory.
 11. The system optimizer of claim 1,wherein processing the user command input comprises determining modelquality of one or more models of components in the enterprise-integratedsystem.
 12. A method, comprising: transmitting a graphicalrepresentation of awell-posed optimization problem comprising aninterconnected network of nodes and connections to an electronic device,wherein the optimization problem relates to a plant in anenterprise-integrated system and comprises a parametric objectivefunction, one or more decision variables, and one or more parametricconstraints, wherein at least one of the nodes comprises a parametrichybrid model that describes how decision variables influence theparametric objective function, at least one connection comprises aparametric hybrid model that describes parametric constraints ondecision variables in connected nodes, and the graphical representationof the optimization problem enables graphically modifying at least oneof the parametric objective function, a decision variable, and aparametric constraint of the optimization problem by interaction with anode or a connection, wherein the parametric hybrid model for a node orconnection comprises: an empirical model configured to generate aparameter model input based at least in part on a variable input to thenode or connection; a parameter model configured to generate afundamental model parameter based at least in part on the parametermodel input and the variable input; and a parametric first principlesmodel configured to generate a variable output of the node or connectionbased at least in part on the variable input and the fundamental modelparameter; receiving a user command input relating to a node orconnection in the graphical representation of the optimization problem;processing the user command input, wherein processing the user commandinput comprises modifying the optimization problem; and executingoptimization with the modified optimization problem to determine currentand future values of decision variables to implement in the plant. 13.The method of claim 12, wherein receiving the user command inputcomprises receiving a user command input to add or delete a node orconnection.
 14. The method of claim 12, wherein modifying theoptimization problem comprises modifying a node parametric hybrid model,a connection parametric hybrid model, or both, wherein the nodeparametric hybrid model is configured to model a component in the plantand the connection parametric hybrid model is configured to model aconstraint in the optimization problem.
 15. The method of claim 12,comprising determining an access level of a user, wherein receiving theuser command input comprises receiving a user command input from anengineer-level user of the plant, and processing the user command inputcomprises modifying one or more parametric hybrid models.
 16. The methodof claim 12, comprising determining an access level of a user, whereinreceiving the user command input comprises receiving a user commandinput from a manager-level user of the plant, and processing the usercommand input comprises modifying a constraint of the optimizationproblem.
 17. The method of claim 16, comprising receiving approval ofthe modified constraint from an engineer-level user of the enterpriseprior to modifying an optimization solution of the optimization problem.18. The method of claim 12, wherein executing the optimization with themodified optimization problem comprises automatically modifying at leastone parametric hybrid model along a predicted operation trajectory ofthe plant modifying the optimization problem to adjust operation of theplant, wherein modifying the optimization problems comprisesconvexification of one or more of the parametric hybrid models along thepredicted operation trajectory, wherein the parameter model, theparametric first-principles model, or both are convexified around thepredicted operation trajectory.
 19. The method of claim 12, whereinexecuting the optimization with the modified optimization problemcomprises determining an optimization solution based at least in part ona previous optimization solution.
 20. A non-transitory computer-readablemedium having computer instructions encoded thereon, wherein thecomputer instructions comprise instructions for: determining an accesslevel of a user operating an electronic device; transmitting a graphicalrepresentation of awell-posed optimization problem comprising aninterconnected network of nodes and connections to an electronic device,wherein the optimization problem relates to an enterprise-integratedsystem and comprises a parametric objective function, one or moredecision variables, and one or more parametric constraints, wherein atleast one of the nodes comprises a parametric hybrid model thatdescribes how decision variables influence the parametric objectivefunction, and at least one connection comprises a parametric hybridmodel that describes parametric constraints on decision variables inconnected nodes, and the graphical representation of the optimizationproblem enables graphically modifying at least one of the parametricobjective function, a decision variable, and a parametric constraint ofthe optimization problem by interaction with a node or a connection,wherein the parametric hybrid model for a node or connection comprises:an empirical model configured to generate a parameter model input basedat least in part on a variable input to the node or connection; aparameter model configured to generate a fundamental model parameterbased at least in part on the parameter model input and the variableinput; and a parametric first principles model configured to generate avariable output of the node or connection based at least in part on thevariable input and the fundamental model parameter; receiving a usercommand input relating to a node or connection in the graphicalrepresentation of the optimization problem; and processing the usercommand input according to the access level of the user, whereinprocessing the user command input comprises modifying the optimizationproblem by convexification of a node parametric hybrid model, aconnection parametric hybrid model, or both, along a predicted operationtrajectory of the enterprise-integrated system, wherein the parametermodel, the parametric first-principle model, or both are convexifiedalong the predicted operation trajectory; and executing optimizationwith the modified optimization problem without adjusting the objectivefunction and the constraints to determine current and future values fordecision variables to implement in the enterprise-integrated systemduring the predicted operation trajectory.